37 citations · 64 across the 14 of their papers we have counts for
15 papers
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Demian Gholipour Ghalandari, Chris Hokamp, Georgiana Ifrim
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sen…
MrSQM: Fast Time Series Classification with Symbolic Representations
Thach Le Nguyen, Georgiana Ifrim
Symbolic representations of time series have proven to be effective for time series classification, with many recent approaches including SAX-VSM, BOSS, WEASEL, and MrSEQL. The key…
Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"
Maria Frizzarin, Antonio Bevilacqua, Bhaskar Dhariyal +11
A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Re…
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
Adrianna Janik, Jonathan Dodd, Georgiana Ifrim +2
Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of a heart condition, which malfunction is the world's leading cause of death. Software…
Background Knowledge Injection for Interpretable Sequence Classification
Severin Gsponer, Luca Costabello, Chan Le Van +4
Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols. Although accuracy is paramount, in certain scen…
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations
Thach Le Nguyen, Severin Gsponer, Iulia Ilie +2
The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused…